Does Audit Committee Accounting Expertise Help to Promote Audit Quality? Evidence from Auditor Reporting of Internal Control Weaknesses
Bibliographic record
Abstract
ABSTRACT In this study, we examine whether audit committee accounting expertise helps to promote audit quality by motivating auditors to conduct diligent internal control audits and make appropriate internal control assessments because audit committee accounting expertise safeguards auditors from dismissal following adverse internal control opinions. Among clients with existing and likely internal control material weaknesses (as proxied by future restatements of audited financial statements), we find a greater likelihood of adverse internal control audit opinions when the audit committee has greater accounting expertise (measured by the proportion of accounting experts on the audit committee). Among all clients, we find a lower likelihood of subsequent auditor dismissal following an adverse internal control audit opinion when the audit committee has greater accounting expertise. In further analyses, we find that this lower likelihood of subsequent auditor dismissal occurs when at least two audit committee members possess accounting expertise. We also find some evidence that CFO influence (but not CEO influence) over the audit committee negates the increased likelihood of adverse internal control opinions when internal control material weaknesses likely exist, as well as the decreased likelihood of auditor dismissal following adverse internal control opinions. These findings have important implications for regulators and corporate nominating committees interested in promoting audit committee effectiveness.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.205 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".